The datasets contains transactions made by credit cards in September 2013 by european cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.
The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation.
Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data.
Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'.
Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning.
Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.m
The term Boosting refers to a family of algorithms which converts weak learner to strong learners.
There are many boosting algorithms:
sklearn.ensemble.GradientBoostingRegressor
xgboost.XGBRegressor # fast and best
lightgbm.LGBMRegressor # extreme fast, little acc than xgb
catboost.CatBoostRegressor # good for categorical feats
%%capture
import sys
ENV_COLAB = 'google.colab' in sys.modules
if ENV_COLAB:
#!pip install hpsklearn
!pip install shap eli5 lime scikit-plot watermark
!pip install optuna hyperopt
!pip install catboost
!pip install ipywidgets
!pip install -U scikit-learn
!jupyter nbextension enable --py widgetsnbextension
# create project like folders
!mkdir -p ../outputs ../images ../reports ../html ../models
print('Environment: Google Colab')
import time
notebook_start_time = time.time()
import numpy as np
import pandas as pd
# random state
SEED = 0
RNG = np.random.RandomState(SEED)
# visualizatioin
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = 8,8
plt.rcParams.update({'font.size': 16})
plt.style.use('ggplot')
%matplotlib inline
import seaborn as sns
sns.set(color_codes=True)
# six and pickle
import six
import pickle
import joblib
# mixed
import copy
import pprint
pp = pprint.PrettyPrinter(indent=4)
# sklearn
import sklearn
# classifiers
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# scale and split
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
# sklearn scalar metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
# roc auc and curves
from sklearn.metrics import auc
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
# confusion matrix and classification report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
# boosting
import xgboost, lightgbm, catboost
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import GradientBoostingRegressor
from xgboost import XGBClassifier, DMatrix
from lightgbm import LGBMClassifier, Dataset
from catboost import CatBoostClassifier, Pool, CatBoost
# parameters tuning
from hyperopt import hp, tpe, fmin, Trials, STATUS_OK, STATUS_FAIL
from hyperopt.pyll import scope
from hyperopt.pyll.stochastic import sample
# model intepretation modules
import eli5
import shap
import yellowbrick
import lime
import scikitplot
# version
%load_ext watermark
%watermark -a "Bhishan Poudel" -d -v -m
print()
%watermark -iv
Bhishan Poudel 2020-10-05 CPython 3.6.9 IPython 5.5.0 compiler : GCC 8.4.0 system : Linux release : 4.19.112+ machine : x86_64 processor : x86_64 CPU cores : 2 interpreter: 64bit sklearn 0.22.2.post1 numpy 1.18.5 xgboost 0.90 catboost 0.24.1 eli5 0.10.1 six 1.15.0 yellowbrick 0.9.1 scikitplot 0.3.7 seaborn 0.11.0 pandas 1.1.2 lightgbm 2.2.3 joblib 0.16.0 shap 0.36.0
def show_method_attributes(obj, ncols=7,start=None, inside=None):
""" Show all the attributes of a given method.
Example:
========
show_method_attributes(list)
"""
lst = [elem for elem in dir(obj) if elem[0]!='_' ]
lst = [elem for elem in lst
if elem not in 'os np pd sys time psycopg2'.split() ]
if isinstance(start,str):
lst = [elem for elem in lst if elem.startswith(start)]
if isinstance(start,tuple) or isinstance(start,list):
lst = [elem for elem in lst for start_elem in start
if elem.startswith(start_elem)]
if isinstance(inside,str):
lst = [elem for elem in lst if inside in elem]
if isinstance(inside,tuple) or isinstance(inside,list):
lst = [elem for elem in lst for inside_elem in inside
if inside_elem in elem]
return pd.DataFrame(np.array_split(lst,ncols)).T.fillna('')
def model_evaluation(model_name, desc, ytest, ypreds,df_eval=None,
show=True,sort_col='Recall'):
if df_eval is None:
df_eval = pd.DataFrame({'Model': [],
'Description':[],
'Accuracy':[],
'Precision':[],
'Recall':[],
'F1':[],
'AUC':[],
})
# model evaluation
average = 'binary'
row_eval = [model_name,desc,
sklearn.metrics.accuracy_score(ytest, ypreds),
sklearn.metrics.precision_score(ytest, ypreds, average=average),
sklearn.metrics.recall_score(ytest, ypreds, average=average),
sklearn.metrics.f1_score(ytest, ypreds, average=average),
sklearn.metrics.roc_auc_score(ytest, ypreds),
]
df_eval.loc[len(df_eval)] = row_eval
df_eval = df_eval.drop_duplicates()
df_eval = df_eval.sort_values(sort_col,ascending=False)
if show:
display(df_eval.style.background_gradient(subset=[sort_col]))
return df_eval
df_eval = None
ifile = 'https://github.com/bhishanpdl/Datasets/blob/master/Projects/Fraud_detection/raw/creditcard.csv.zip?raw=true'
df = pd.read_csv(ifile,compression='zip')
print(df.shape)
df.head()
(284807, 31)
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | 0.090794 | -0.551600 | -0.617801 | -0.991390 | -0.311169 | 1.468177 | -0.470401 | 0.207971 | 0.025791 | 0.403993 | 0.251412 | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
| 1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | -0.166974 | 1.612727 | 1.065235 | 0.489095 | -0.143772 | 0.635558 | 0.463917 | -0.114805 | -0.183361 | -0.145783 | -0.069083 | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
| 2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | 0.207643 | 0.624501 | 0.066084 | 0.717293 | -0.165946 | 2.345865 | -2.890083 | 1.109969 | -0.121359 | -2.261857 | 0.524980 | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
| 3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | -0.054952 | -0.226487 | 0.178228 | 0.507757 | -0.287924 | -0.631418 | -1.059647 | -0.684093 | 1.965775 | -1.232622 | -0.208038 | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
| 4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | 0.753074 | -0.822843 | 0.538196 | 1.345852 | -1.119670 | 0.175121 | -0.451449 | -0.237033 | -0.038195 | 0.803487 | 0.408542 | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
target = 'Class'
features = df.columns.drop(target)
df[target].value_counts(normalize=True)*100
0 99.827251 1 0.172749 Name: Class, dtype: float64
sns.countplot(x=df[target])
<matplotlib.axes._subplots.AxesSubplot at 0x7f67d3bc8f28>
from sklearn.model_selection import train_test_split
df_Xtrain_orig, df_Xtest, ser_ytrain_orig, ser_ytest = train_test_split(
df.drop(target,axis=1),
df[target],
test_size=0.2,
random_state=SEED,
stratify=df[target])
ytrain_orig = ser_ytrain_orig.to_numpy().ravel()
ytest = ser_ytest.to_numpy().ravel()
print(df_Xtrain_orig.shape)
df_Xtrain_orig.head()
(227845, 30)
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211885 | 138616.0 | -1.137612 | 2.345154 | -1.767247 | 0.833982 | 0.973168 | -0.073571 | 0.802433 | 0.733137 | -1.154087 | -0.520340 | 0.494117 | 0.799935 | 0.494576 | -0.479666 | -0.917177 | -0.184117 | 1.189459 | 0.937244 | 0.960749 | 0.062820 | 0.114953 | 0.430613 | -0.240819 | 0.124011 | 0.187187 | -0.402251 | 0.196277 | 0.190732 | 39.46 |
| 12542 | 21953.0 | -1.028649 | 1.141569 | 2.492561 | -0.242233 | 0.452842 | -0.384273 | 1.256026 | -0.816401 | 1.964560 | -0.014216 | 0.432153 | -2.140921 | 2.274477 | 0.114128 | -1.652894 | -0.617302 | 0.243791 | -0.426168 | -0.493177 | 0.350032 | -0.380356 | -0.037432 | -0.503934 | 0.407129 | 0.604252 | 0.233015 | -0.433132 | -0.491892 | 7.19 |
| 270932 | 164333.0 | -1.121864 | -0.195099 | 1.282634 | -3.172847 | -0.761969 | -0.287013 | -0.586367 | 0.496182 | -2.352349 | 0.350551 | -1.319688 | -0.942001 | 1.082210 | -0.425735 | 0.036748 | 0.380392 | -0.033353 | 0.204609 | -0.801465 | -0.113632 | -0.328953 | -0.856937 | -0.056198 | 0.401905 | 0.406813 | -0.440140 | 0.152356 | 0.030128 | 40.00 |
| 30330 | 35874.0 | 1.094238 | -0.760568 | -0.392822 | -0.611720 | -0.722850 | -0.851978 | -0.185505 | -0.095131 | -1.122304 | 0.367009 | 1.378493 | -0.724216 | -1.105406 | -0.480170 | 0.220826 | 1.745743 | 0.740817 | -0.728827 | 1.016740 | 0.354148 | -0.227392 | -1.254285 | 0.022116 | -0.141531 | 0.114515 | -0.652427 | -0.037897 | 0.051254 | 165.85 |
| 272477 | 165107.0 | 2.278095 | -1.298924 | -1.884035 | -1.530435 | -0.649500 | -0.996024 | -0.466776 | -0.438025 | -1.612665 | 1.631133 | -1.126000 | -0.938760 | 0.300621 | -0.119667 | -0.585453 | -1.106244 | 0.690235 | -0.124401 | -0.075649 | -0.341708 | 0.123892 | 0.815909 | -0.072537 | 0.784217 | 0.403428 | 0.193747 | -0.043185 | -0.058719 | 60.00 |
df_Xtrain, df_Xvalid, ser_ytrain, ser_yvalid = train_test_split(
df_Xtrain_orig,
ser_ytrain_orig,
test_size=0.2,
random_state=SEED,
stratify=ser_ytrain_orig)
ytrain = ser_ytrain.to_numpy().ravel()
yvalid = ser_yvalid.to_numpy().ravel()
print(df_Xtrain.shape)
(182276, 30)
https://catboost.ai/docs/concepts/python-reference_catboostregressor.html
class CatBoostRegressor(
iterations=None, learning_rate=None,
depth=None, l2_leaf_reg=None,
model_size_reg=None, rsm=None,
loss_function='RMSE', border_count=None,
feature_border_type=None, per_float_feature_quantization=None,
input_borders=None, output_borders=None,
fold_permutation_block=None, od_pval=None,
od_wait=None, od_type=None,
nan_mode=None, counter_calc_method=None,
leaf_estimation_iterations=None, leaf_estimation_method=None,
thread_count=None, random_seed=None,
use_best_model=None, best_model_min_trees=None,
verbose=None, silent=None,
logging_level=None, metric_period=None,
ctr_leaf_count_limit=None, store_all_simple_ctr=None,
max_ctr_complexity=None, has_time=None,
allow_const_label=None, one_hot_max_size=None,
random_strength=None,name=None, ignored_features=None,
train_dir=None, custom_metric=None,
eval_metric=None, bagging_temperature=None,
save_snapshot=None, snapshot_file=None,
snapshot_interval=None, fold_len_multiplier=None,
used_ram_limit=None, gpu_ram_part=None,
pinned_memory_size=None, allow_writing_files=None,
final_ctr_computation_mode=None, approx_on_full_history=None,
boosting_type=None, simple_ctr=None,
combinations_ctr=None, per_feature_ctr=None,
ctr_target_border_count=None, task_type=None,
device_config=None, devices=None,
bootstrap_type=None, subsample=None,
sampling_unit=None, dev_score_calc_obj_block_size=None,
max_depth=None, n_estimators=None,
num_boost_round=None, num_trees=None,
colsample_bylevel=None, random_state=None,
reg_lambda=None, objective=None,
eta=None, max_bin=None,
gpu_cat_features_storage=None, data_partition=None,
metadata=None, early_stopping_rounds=None,
cat_features=None, grow_policy=None,
min_data_in_leaf=None, min_child_samples=None,
max_leaves=None, num_leaves=None,
score_function=None, leaf_estimation_backtracking=None,
ctr_history_unit=None, monotone_constraints=None
)
import catboost
show_method_attributes(catboost,2)
| 0 | 1 | |
|---|---|---|
| 0 | CatBoost | Pool |
| 1 | CatBoostClassifier | core |
| 2 | CatBoostError | cv |
| 3 | CatBoostRegressor | sum_models |
| 4 | CatboostError | to_classifier |
| 5 | EFstrType | to_regressor |
| 6 | FeaturesData | train |
| 7 | MetricVisualizer | version |
| 8 | MultiRegressionCustomMetric | widget |
| 9 | MultiRegressionCustomObjective |
from catboost import CatBoostClassifier, Pool
show_method_attributes(CatBoostClassifier,2)
| 0 | 1 | |
|---|---|---|
| 0 | best_iteration_ | get_test_evals |
| 1 | best_score_ | get_text_feature_indices |
| 2 | calc_feature_statistics | get_tree_leaf_counts |
| 3 | calc_leaf_indexes | grid_search |
| 4 | classes_ | is_fitted |
| 5 | compare | iterate_leaf_indexes |
| 6 | copy | learning_rate_ |
| 7 | create_metric_calcer | load_model |
| 8 | drop_unused_features | n_features_in_ |
| 9 | eval_metrics | plot_partial_dependence |
| 10 | evals_result_ | plot_predictions |
| 11 | feature_importances_ | plot_tree |
| 12 | feature_names_ | predict |
| 13 | fit | predict_log_proba |
| 14 | get_all_params | predict_proba |
| 15 | get_best_iteration | random_seed_ |
| 16 | get_best_score | randomized_search |
| 17 | get_borders | save_borders |
| 18 | get_cat_feature_indices | save_model |
| 19 | get_embedding_feature_indices | score |
| 20 | get_evals_result | set_feature_names |
| 21 | get_feature_importance | set_leaf_values |
| 22 | get_leaf_values | set_params |
| 23 | get_leaf_weights | set_scale_and_bias |
| 24 | get_metadata | shrink |
| 25 | get_n_features_in | staged_predict |
| 26 | get_object_importance | staged_predict_log_proba |
| 27 | get_param | staged_predict_proba |
| 28 | get_params | tree_count_ |
| 29 | get_scale_and_bias | virtual_ensembles_predict |
| 30 | get_test_eval |
from catboost import CatBoostClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.metrics import accuracy_score, precision_score, recall_score,f1_score
from sklearn.metrics import confusion_matrix
# time
time_start = time.time()
# current parameters
desc = 'default,random_state=100, cross_validation_ypreds'
Xtr = df_Xtrain.to_numpy()
ytr = ser_ytrain.to_numpy().ravel()
Xtx = df_Xtest.to_numpy()
ytx = ser_ytest.to_numpy().ravel()
# fit the model
model = CatBoostClassifier(verbose=100,random_state=SEED)
model.fit(Xtr, ytr)
# save the model
# joblib.dump(model_cat, 'model_cat.pkl')
# model_cat = joblib.load('model_cat.pkl')
# predictions
skf = StratifiedKFold(n_splits=2,shuffle=True,random_state=SEED)
ypreds_cv = cross_val_predict(model, Xtx, ytx, cv=skf)
ypreds = ypreds_cv
# model evaluation
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
time_taken = time.time() - time_start
print('Time taken: {:.0f} min {:.0f} secs'.format(*divmod(time_taken,60)))
display(df_eval)
Learning rate set to 0.095119 0: learn: 0.4064239 total: 82.3ms remaining: 1m 22s 100: learn: 0.0014365 total: 7.97s remaining: 1m 10s 200: learn: 0.0009553 total: 15.8s remaining: 1m 2s 300: learn: 0.0006789 total: 23.9s remaining: 55.4s 400: learn: 0.0004701 total: 31.7s remaining: 47.4s 500: learn: 0.0003296 total: 39.7s remaining: 39.5s 600: learn: 0.0002371 total: 47.8s remaining: 31.7s 700: learn: 0.0001719 total: 55.6s remaining: 23.7s 800: learn: 0.0001354 total: 1m 3s remaining: 15.7s 900: learn: 0.0001058 total: 1m 11s remaining: 7.83s 999: learn: 0.0000872 total: 1m 19s remaining: 0us Learning rate set to 0.043056 0: learn: 0.5635981 total: 26.5ms remaining: 26.5s 100: learn: 0.0017777 total: 2.33s remaining: 20.7s 200: learn: 0.0007480 total: 4.63s remaining: 18.4s 300: learn: 0.0003691 total: 6.92s remaining: 16.1s 400: learn: 0.0002480 total: 9.17s remaining: 13.7s 500: learn: 0.0001734 total: 11.5s remaining: 11.4s 600: learn: 0.0001340 total: 13.7s remaining: 9.09s 700: learn: 0.0001134 total: 15.9s remaining: 6.79s 800: learn: 0.0000977 total: 18.2s remaining: 4.51s 900: learn: 0.0000858 total: 20.4s remaining: 2.24s 999: learn: 0.0000764 total: 22.7s remaining: 0us Learning rate set to 0.043056 0: learn: 0.5628806 total: 25.7ms remaining: 25.6s 100: learn: 0.0019698 total: 2.34s remaining: 20.8s 200: learn: 0.0009914 total: 4.66s remaining: 18.5s 300: learn: 0.0005191 total: 6.99s remaining: 16.2s 400: learn: 0.0003353 total: 9.31s remaining: 13.9s 500: learn: 0.0002580 total: 11.6s remaining: 11.6s 600: learn: 0.0002056 total: 13.9s remaining: 9.22s 700: learn: 0.0001702 total: 16.2s remaining: 6.91s 800: learn: 0.0001445 total: 18.5s remaining: 4.59s 900: learn: 0.0001232 total: 20.8s remaining: 2.29s 999: learn: 0.0001062 total: 23.1s remaining: 0us
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.913580 | 0.755102 | 0.826816 | 0.877489 |
Time taken: 2 min 7 secs
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.91358 | 0.755102 | 0.826816 | 0.877489 |
%%time
model = CatBoostClassifier(verbose=100,random_state=SEED)
model.fit(Xtr, ytr)
joblib.dump(model, '../models/model_cat_default_seed100.joblib')
ypreds = model.predict(Xtx)
cm = sklearn.metrics.confusion_matrix(ytx,ypres)
print('confusion matrix\n',cm)
desc = 'default, seed=100'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
[0 0 0 0 0] [56885 77]
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.913580 | 0.755102 | 0.826816 | 0.877489 |
| 1 | catboost | early stopping, iterations=885 | 0.999579 | 0.986842 | 0.765306 | 0.862069 | 0.882644 |
| 2 | catboost | grid search optuna | 0.999579 | 0.974359 | 0.775510 | 0.863636 | 0.887738 |
| 3 | catboost | default, seed=100 | 0.999631 | 1.000000 | 0.785714 | 0.880000 | 0.892857 |
CPU times: user 227 ms, sys: 8.09 ms, total: 235 ms Wall time: 179 ms
yprobs = model.predict_proba(Xtx)
print(yprobs[:5])
[[9.99972750e-01 2.72503683e-05] [9.99996518e-01 3.48188471e-06] [9.99998383e-01 1.61719094e-06] [9.99995585e-01 4.41504877e-06] [9.99989143e-01 1.08570064e-05]]
from scikitplot import metrics as skpmetrics
skpmetrics.plot_confusion_matrix(ytx, ypreds)
<matplotlib.axes._subplots.AxesSubplot at 0x7f67d0169ba8>
fig, ax = plt.subplots(figsize=(12,8))
skpmetrics.plot_roc(ytx,yprobs,ax=ax)
<matplotlib.axes._subplots.AxesSubplot at 0x7f67d003ff28>
import eli5
# eli5.explain_weights_catboost(model) # same thing
eli5.show_weights(model)
| Weight | Feature |
|---|---|
| 0.0776 | 4 |
| 0.0752 | 1 |
| 0.0671 | 14 |
| 0.0566 | 0 |
| 0.0495 | 8 |
| 0.0462 | 9 |
| 0.0451 | 26 |
| 0.0430 | 12 |
| 0.0385 | 2 |
| 0.0378 | 29 |
| 0.0346 | 10 |
| 0.0323 | 19 |
| 0.0318 | 24 |
| 0.0297 | 6 |
| 0.0281 | 11 |
| 0.0280 | 28 |
| 0.0278 | 13 |
| 0.0243 | 25 |
| 0.0237 | 15 |
| 0.0236 | 18 |
| … 10 more … | |
df_Xtrain.head(2)
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35574 | 38177.0 | 1.430419 | -0.718078 | 0.364706 | -0.744257 | -0.556090 | 0.698948 | -0.949852 | 0.131008 | -0.314353 | 0.512322 | -0.202255 | 0.766648 | 1.495082 | -1.037475 | -1.935434 | 0.897715 | 0.069580 | -0.902556 | 1.843867 | 0.158424 | 0.042013 | 0.429576 | -0.301931 | -0.933773 | 0.840490 | -0.027776 | 0.044688 | -0.007522 | 0.2 |
| 46862 | 42959.0 | -2.425523 | -1.790293 | 2.522139 | 0.581141 | 0.918453 | 0.594426 | 0.224541 | 0.373885 | -0.168411 | -0.720421 | 1.394710 | 1.136436 | 0.508455 | -0.389067 | -0.165166 | -0.040520 | -0.464966 | -0.057803 | -1.493635 | 0.984535 | 0.538438 | 0.877560 | 0.590595 | -0.293545 | 0.524022 | -0.328189 | -0.205285 | -0.109163 | 300.0 |
# # time
# time_start = time.time()
# # current parameters
# Xtr = df_Xtrain
# ytr = ser_ytrain.to_numpy().ravel()
# Xtx = df_Xtest
# ytx = ser_ytest.to_numpy().ravel()
# Xvd = df_Xvalid
# yvd = ser_yvalid.to_numpy().ravel()
# # fit the model
# model = CatBoostClassifier(random_state=0,verbose=100)
# model.fit(Xtr, ytr,
# eval_set=(Xvd, yvd))
# # ypreds
# ypreds = model.predict(Xtx)
# # r-squared values
# auc = roc_auc_score(ytx, ypreds)
# # time
# time_taken = time.time() - time_start
# print('Time taken: {:.0f} min {:.0f} secs'.format(*divmod(time_taken,60)))
# print('ROC AUC Score ', auc)
catboost tutorials model analysis feature statistics tutorial
# float feature
feature_name = 'Amount'
dict_stats = model.calc_feature_statistics(df_Xtrain, ser_ytrain, feature_name, plot=True)
# feature importance
df_imp = pd.DataFrame({'Feature': features,
'Importance': model.feature_importances_
})
df_imp.sort_values('Importance',ascending=False).style.background_gradient()
| Feature | Importance | |
|---|---|---|
| 4 | V4 | 9.231178 |
| 1 | V1 | 8.884553 |
| 12 | V12 | 7.858850 |
| 14 | V14 | 6.993834 |
| 8 | V8 | 5.448198 |
| 0 | Time | 5.067847 |
| 26 | V26 | 4.613358 |
| 11 | V11 | 3.664417 |
| 16 | V16 | 3.448854 |
| 6 | V6 | 3.389446 |
| 7 | V7 | 3.175937 |
| 29 | Amount | 3.115607 |
| 18 | V18 | 3.041911 |
| 10 | V10 | 2.904488 |
| 17 | V17 | 2.794143 |
| 25 | V25 | 2.587967 |
| 27 | V27 | 2.485769 |
| 19 | V19 | 2.457251 |
| 15 | V15 | 2.322782 |
| 2 | V2 | 2.310883 |
| 20 | V20 | 2.079404 |
| 13 | V13 | 1.815875 |
| 28 | V28 | 1.786981 |
| 3 | V3 | 1.770443 |
| 24 | V24 | 1.469989 |
| 22 | V22 | 1.362665 |
| 9 | V9 | 1.172512 |
| 5 | V5 | 1.104829 |
| 23 | V23 | 0.909606 |
| 21 | V21 | 0.730423 |
def plot_feature_imp_catboost(model_catboost,features):
"""Plot the feature importance horizontal bar plot.
"""
df_imp = pd.DataFrame({'Feature': model.feature_names_,
'Importance': model.feature_importances_
})
df_imp = df_imp.sort_values('Importance').set_index('Feature')
ax = df_imp.plot.barh(figsize=(12,8))
plt.grid(True)
plt.title('Feature Importance',fontsize=14)
ax.get_legend().remove()
for p in ax.patches:
x = p.get_width()
y = p.get_y()
text = '{:.2f}'.format(p.get_width())
ax.text(x, y,text,fontsize=15,color='indigo')
plt.show()
plot_feature_imp_catboost(model, features)
df_fimp = model.get_feature_importance(prettified=True)
df_fimp.head()
| Feature Id | Importances | |
|---|---|---|
| 0 | V4 | 9.231178 |
| 1 | V1 | 8.884553 |
| 2 | V12 | 7.858850 |
| 3 | V14 | 6.993834 |
| 4 | V8 | 5.448198 |
plt.figure(figsize=(12,8))
ax = sns.barplot(x=df_fimp.columns[1], y=df_fimp.columns[0], data=df_fimp);
for p in ax.patches:
x = p.get_width()
y = p.get_y()
text = '{:.2f}'.format(p.get_width())
ax.text(x, y,text,fontsize=15,color='indigo',va='top',ha='left')
from catboost import CatBoost, Pool
# help(CatBoost)
cat_features = [] # take it empty for the moment
dtrain = Pool(df_Xtrain, ser_ytrain, cat_features=cat_features)
dvalid = Pool(df_Xvalid, ser_yvalid, cat_features=cat_features)
dtest = Pool(df_Xtest, ser_ytest, cat_features=cat_features)
params_cat = {'iterations': 100, 'verbose': False,
'random_seed': 0,
'eval_metric':'AUC',
'loss_function':'Logloss',
'cat_features': [],
'ignored_features': [],
'early_stopping_rounds': 200,
'verbose': 200,
}
bst_cat = CatBoost(params=params_cat)
bst_cat.fit(dtrain,
eval_set=(df_Xvalid, ser_yvalid),
use_best_model=True,
plot=True);
print(bst_cat.eval_metrics(dtest, ['AUC'])['AUC'][-1])
Learning rate set to 0.312111 0: test: 0.9426860 best: 0.9426860 (0) total: 92.2ms remaining: 9.13s 99: test: 0.9732950 best: 0.9804994 (14) total: 8.56s remaining: 0us bestTest = 0.9804994003 bestIteration = 14 Shrink model to first 15 iterations. 0.9632516501958127
cv(pool=None, params=None, dtrain=None, iterations=None,
num_boost_round=None, fold_count=None, nfold=None, inverted=False,
partition_random_seed=0, seed=None, shuffle=True, logging_level=None,
stratified=None, as_pandas=True, metric_period=None, verbose=None,
verbose_eval=None, plot=False, early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None,
snapshot_interval=None, folds=None, type='Classical')
params = {'iterations': 100, 'verbose': False,
'random_seed': 0,
'loss_function':'Logloss',
'eval_metric':'AUC',
}
df_scores = catboost.cv(dtrain,
params,
fold_count=2,
verbose=100,
shuffle=True,
stratified=True,
plot="True") # plot does not work in google colab
0: test: 0.9182109 best: 0.9182109 (0) total: 227ms remaining: 22.5s 99: test: 0.9769374 best: 0.9792743 (56) total: 17.9s remaining: 0us
print(df_scores.columns)
df_scores.head()
Index(['iterations', 'test-AUC-mean', 'test-AUC-std', 'test-Logloss-mean',
'test-Logloss-std', 'train-Logloss-mean', 'train-Logloss-std'],
dtype='object')
| iterations | test-AUC-mean | test-AUC-std | test-Logloss-mean | test-Logloss-std | train-Logloss-mean | train-Logloss-std | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0.918211 | 0.015632 | 0.585840 | 0.001246 | 0.585823 | 0.001171 |
| 1 | 1 | 0.922383 | 0.027860 | 0.500689 | 0.002353 | 0.500659 | 0.002239 |
| 2 | 2 | 0.933871 | 0.022411 | 0.425035 | 0.003157 | 0.425024 | 0.003205 |
| 3 | 3 | 0.928061 | 0.020897 | 0.365778 | 0.003360 | 0.365737 | 0.003457 |
| 4 | 4 | 0.939572 | 0.017085 | 0.310018 | 0.004005 | 0.309959 | 0.003970 |
sns.lineplot(x='iterations',y='train-Logloss-mean',data=df_scores,ax=ax,color='r')
sns.lineplot(x='iterations',y='test-Logloss-mean',data=df_scores,ax=ax,
color='b',alpha=0.2,linewidth=5,linestyle='--')
plt.show()
We generally should optimize model complexity and then tune the convergence.
model complexity: max_depth etc convergence: learning rate
Parameters:
model = joblib.load('../models/model_cat_default_seed100.joblib')
ypreds = model.predict(df_Xtest)
cm = confusion_matrix(ytest, ypreds)
print(cm)
[[56864 0] [ 21 77]]
%%time
params = dict(verbose=500,
random_state=0,
iterations=3_000,
eval_metric='AUC',
cat_features = [],
early_stopping_rounds=200,
)
model = catboost.CatBoostClassifier(**params)
model.fit(df_Xtrain, ytrain,
eval_set=(df_Xvalid, yvalid),
use_best_model=True,
plot=False
);
# now use the best iteration
best_iter = model.get_best_iteration()
model = CatBoostClassifier(verbose=False,random_state=0,iterations=best_iter)
model.fit(df_Xtrain, ser_ytrain)
joblib.dump(model, '../models/model_cat_earlystopping.joblib')
ypreds = model.predict(df_Xtest)
cm = confusion_matrix(ytest, ypreds)
print(cm)
desc = f'early stopping, iterations={best_iter}'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
# using best iterations is worse, use previous 1000.
[[56863 1] [ 23 75]]
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.913580 | 0.755102 | 0.826816 | 0.877489 |
| 1 | catboost | early stopping, iterations=885 | 0.999579 | 0.986842 | 0.765306 | 0.862069 | 0.882644 |
CPU times: user 2min 11s, sys: 6.69 s, total: 2min 18s Wall time: 1min 11s
# for n in [6]: # default detpth = 6
# model = CatBoostClassifier(verbose=False,random_state=0,
# iterations=1_000,
# depth=n,
# )
# model.fit(Xtr, ytr)
# ypreds = model.predict(Xtx)
# cm = confusion_matrix(ytest, ypreds)
# error = cm[0,1] + cm[1,0]
# print(f'Confusion matrix error count = {error} for n = {n}')
# for n in [0]:
# model = CatBoostClassifier(verbose=False,random_state=n,
# depth=6,
# iterations=1_000,
# )
# model.fit(Xtr, ytr)
# ypreds = model.predict(Xtx)
# cm = confusion_matrix(ytest, ypreds)
# error = cm[0,1] + cm[1,0]
# print(f'Confusion matrix error count = {error} for n = {n}')
import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING) # use INFO to see progress
def objective(trial):
params_cat_optuna = {
'objective': trial.suggest_categorical('objective', ['Logloss', 'CrossEntropy']),
'colsample_bylevel': trial.suggest_uniform('colsample_bylevel', 0.01, 0.1),
'depth': trial.suggest_int('depth', 1, 12),
'boosting_type': trial.suggest_categorical('boosting_type', ['Ordered', 'Plain']),
'bootstrap_type': trial.suggest_categorical('bootstrap_type',
['Bayesian', 'Bernoulli', 'MVS']),
'used_ram_limit': '3gb'
}
# update parameters
if params_cat_optuna['bootstrap_type'] == 'Bayesian':
params_cat_optuna['bagging_temperature'] = trial.suggest_uniform('bagging_temperature', 0, 10)
elif params_cat_optuna['bootstrap_type'] == 'Bernoulli':
params_cat_optuna['subsample'] = trial.suggest_uniform('subsample', 0.1, 1)
# fit the model
model = CatBoostClassifier(random_state=SEED,**params_cat_optuna)
model.fit(df_Xtrain, ser_ytrain,
eval_set=[(df_Xvalid, ser_yvalid)],
verbose=0,
early_stopping_rounds=100)
ypreds = model.predict(df_Xvalid)
ypreds = np.rint(ypreds)
score = roc_auc_score(ser_yvalid.to_numpy().ravel(),
ypreds)
return score
# NOTE: there is inherent non-determinism in optuna hyperparameter selection
# we may not get the same hyperparameters when run twice.
sampler = optuna.samplers.TPESampler(seed=SEED)
N_TRIALS = 1 # make it large
study = optuna.create_study(direction='maximize',
sampler=sampler,
study_name='cat_optuna',
storage='sqlite:///cat_optuna_fraud_detection.db',
load_if_exists=True)
study.optimize(objective, n_trials=N_TRIALS,timeout=600)
# Resume from last time
sampler = optuna.samplers.TPESampler(seed=SEED)
N_TRIALS = 1 # make it large
study = optuna.create_study(direction='maximize',
sampler=sampler,
study_name='cat_optuna',
storage='sqlite:///cat_optuna_fraud_detection.db',
load_if_exists=True)
# study.optimize(objective, n_trials=N_TRIALS)
print(f'Number of finished trials: {len(study.trials)}')
# best trail
best_trial = study.best_trial
# best params
params_best = study.best_trial.params
params_best
Number of finished trials: 2
{'bagging_temperature': 1.4860484007536512,
'boosting_type': 'Plain',
'bootstrap_type': 'Bayesian',
'colsample_bylevel': 0.07040400702545975,
'depth': 8,
'objective': 'Logloss'}
%%time
model_name = 'catboost'
desc = 'grid search optuna'
Xtr = df_Xtrain_orig
ytr = ser_ytrain_orig.to_numpy().ravel()
Xtx = df_Xtest
ytx = ser_ytest.to_numpy().ravel()
Xvd = df_Xvalid
yvd = ser_yvalid.to_numpy().ravel()
# use best model
params_best = study.best_trial.params
clf = CatBoostClassifier(random_state=SEED,verbose=False)
clf.set_params(**params_best)
# fit and save the model
clf.fit(Xtr, ytr)
joblib.dump(clf,'../models/clf_cat_grid_search_optuna.pkl')
# load the saved model
clf = joblib.load('../models/clf_cat_grid_search_optuna.pkl')
# predictions
ypreds = clf.predict(Xtx)
# model evaluation
cm = confusion_matrix(ytx, ypreds)
print(cm)
desc = f'grid search optuna'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
[[56862 2] [ 22 76]]
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.913580 | 0.755102 | 0.826816 | 0.877489 |
| 1 | catboost | early stopping, iterations=885 | 0.999579 | 0.986842 | 0.765306 | 0.862069 | 0.882644 |
| 2 | catboost | grid search optuna | 0.999579 | 0.974359 | 0.775510 | 0.863636 | 0.887738 |
CPU times: user 2min 19s, sys: 7.3 s, total: 2min 26s Wall time: 1min 16s
%%time
model = CatBoostClassifier(verbose=False,random_state=100,
depth=6,
iterations=1_000,
)
model.fit(Xtr, ytr)
joblib.dump(model, '../models/model_cat_best.joblib')
ypreds = model.predict(Xtx)
cm = confusion_matrix(ytest, ypreds)
print(cm)
df_eval = model_evaluation('catboost', 'seed=100,depth=6,iter=1k', ytest, ypreds,df_eval=df_eval)
[[56864 0] [ 21 77]]
| Model | Description | Accuracy | Precision | Recall | F1 | AUC | |
|---|---|---|---|---|---|---|---|
| 0 | catboost | default,random_state=100, numpy | 0.999456 | 0.913580 | 0.755102 | 0.826816 | 0.877489 |
| 1 | catboost | early stopping, iterations=885 | 0.999579 | 0.986842 | 0.765306 | 0.862069 | 0.882644 |
| 2 | catboost | grid search optuna | 0.999579 | 0.974359 | 0.775510 | 0.863636 | 0.887738 |
| 3 | catboost | default, seed=100 | 0.999631 | 1.000000 | 0.785714 | 0.880000 | 0.892857 |
| 4 | catboost | seed=100,depth=6,iter=1k | 0.999631 | 1.000000 | 0.785714 | 0.880000 | 0.892857 |
CPU times: user 3min, sys: 7.99 s, total: 3min 8s Wall time: 1min 36s
df_Xtrain.head(2).append(df_Xtest.head(2))
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35574 | 38177.0 | 1.430419 | -0.718078 | 0.364706 | -0.744257 | -0.556090 | 0.698948 | -0.949852 | 0.131008 | -0.314353 | 0.512322 | -0.202255 | 0.766648 | 1.495082 | -1.037475 | -1.935434 | 0.897715 | 0.069580 | -0.902556 | 1.843867 | 0.158424 | 0.042013 | 0.429576 | -0.301931 | -0.933773 | 0.840490 | -0.027776 | 0.044688 | -0.007522 | 0.20 |
| 46862 | 42959.0 | -2.425523 | -1.790293 | 2.522139 | 0.581141 | 0.918453 | 0.594426 | 0.224541 | 0.373885 | -0.168411 | -0.720421 | 1.394710 | 1.136436 | 0.508455 | -0.389067 | -0.165166 | -0.040520 | -0.464966 | -0.057803 | -1.493635 | 0.984535 | 0.538438 | 0.877560 | 0.590595 | -0.293545 | 0.524022 | -0.328189 | -0.205285 | -0.109163 | 300.00 |
| 248750 | 154078.0 | 0.046622 | 1.529678 | -0.453615 | 1.282569 | 1.110333 | -0.882716 | 1.046420 | -0.117121 | -0.679897 | -0.923709 | 0.371519 | -0.000047 | 0.512255 | -2.091762 | 0.786796 | 0.159652 | 1.706939 | 0.458922 | 0.037665 | 0.240559 | -0.338472 | -0.839547 | 0.066527 | 0.836447 | 0.076790 | -0.775158 | 0.261012 | 0.058359 | 18.70 |
| 161573 | 114332.0 | 0.145870 | 0.107484 | 0.755127 | -0.995936 | 1.159107 | 2.113961 | 0.036200 | 0.471777 | 0.627622 | -0.598398 | 0.713816 | 1.091294 | 0.663878 | -0.448057 | 0.146422 | -0.445603 | -0.462439 | -0.373996 | -0.966334 | -0.107332 | 0.297644 | 1.285809 | -0.140560 | -0.910706 | -0.449729 | -0.235203 | -0.036910 | -0.227111 | 9.99 |
import eli5
eli5.show_weights(model)
| Weight | Feature |
|---|---|
| 0.1009 | V1 |
| 0.0653 | V4 |
| 0.0641 | V14 |
| 0.0604 | V26 |
| 0.0542 | Amount |
| 0.0389 | V12 |
| 0.0371 | V15 |
| 0.0369 | V10 |
| 0.0354 | V11 |
| 0.0333 | Time |
| 0.0298 | V8 |
| 0.0297 | V19 |
| 0.0281 | V13 |
| 0.0274 | V7 |
| 0.0273 | V20 |
| 0.0267 | V2 |
| 0.0255 | V3 |
| 0.0254 | V22 |
| 0.0253 | V16 |
| 0.0247 | V18 |
| … 10 more … | |
from eli5.sklearn import PermutationImportance
feature_names = df_Xtrain.columns.tolist()
perm = PermutationImportance(model).fit(df_Xtest, ytx)
eli5.show_weights(perm, feature_names=feature_names)
| Weight | Feature |
|---|---|
| 0.0008 ± 0.0000 | V14 |
| 0.0003 ± 0.0000 | V4 |
| 0.0002 ± 0.0000 | V10 |
| 0.0001 ± 0.0001 | V26 |
| 0.0001 ± 0.0001 | Amount |
| 0.0001 ± 0.0000 | V28 |
| 0.0001 ± 0.0000 | V12 |
| 0.0001 ± 0.0000 | V17 |
| 0.0001 ± 0.0000 | V16 |
| 0.0000 ± 0.0001 | V1 |
| 0.0000 ± 0.0000 | V22 |
| 0.0000 ± 0.0000 | V19 |
| 0.0000 ± 0.0000 | V27 |
| 0.0000 ± 0.0000 | V20 |
| 0.0000 ± 0.0000 | V8 |
| 0.0000 ± 0.0000 | V6 |
| 0.0000 ± 0.0000 | V3 |
| 0.0000 ± 0.0000 | V5 |
| 0.0000 ± 0.0000 | V7 |
| 0.0000 ± 0.0000 | V25 |
| … 10 more … | |
import lime
import lime.lime_tabular
idx = 0
example = df_Xtest.iloc[idx]
answer = ser_ytest.iloc[idx]
feature_names = df_Xtest.columns.tolist()
prediction = model.predict(example.to_numpy().reshape(-1,1).T)
print(f'answer = {answer}')
print('prediction = ', prediction[0])
print()
print(example)
print(feature_names)
answer = 0 prediction = 0 Time 154078.000000 V1 0.046622 V2 1.529678 V3 -0.453615 V4 1.282569 V5 1.110333 V6 -0.882716 V7 1.046420 V8 -0.117121 V9 -0.679897 V10 -0.923709 V11 0.371519 V12 -0.000047 V13 0.512255 V14 -2.091762 V15 0.786796 V16 0.159652 V17 1.706939 V18 0.458922 V19 0.037665 V20 0.240559 V21 -0.338472 V22 -0.839547 V23 0.066527 V24 0.836447 V25 0.076790 V26 -0.775158 V27 0.261012 V28 0.058359 Amount 18.700000 Name: 248750, dtype: float64 ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']
import lime
import lime.lime_tabular
categorical_features = []
categorical_features_idx = [df_Xtrain.columns.get_loc(col) for col in categorical_features]
explainer = lime.lime_tabular.LimeTabularExplainer(df_Xtrain.to_numpy(),
feature_names=feature_names,
class_names=['Not-fraud','Fraud'],
categorical_features=categorical_features_idx,
mode='classification')
exp = explainer.explain_instance(example, model.predict_proba, num_features=8)
exp.show_in_notebook(show_table=True)
exp.as_pyplot_figure(); # use semicolon
import shap
shap.initjs()
# model = CatBoostClassifier(verbose=100,random_state=100)
# model.fit(df_Xtrain, ytrain)
model = joblib.load('../models/model_cat_best.joblib')
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(df_Xtest)
Learning rate set to 0.095119 0: learn: 0.4064239 total: 84.1ms remaining: 1m 24s 100: learn: 0.0014365 total: 8.01s remaining: 1m 11s 200: learn: 0.0009553 total: 15.9s remaining: 1m 3s 300: learn: 0.0006789 total: 23.9s remaining: 55.4s 400: learn: 0.0004701 total: 31.9s remaining: 47.6s 500: learn: 0.0003296 total: 39.7s remaining: 39.6s 600: learn: 0.0002371 total: 47.7s remaining: 31.7s 700: learn: 0.0001719 total: 55.5s remaining: 23.7s 800: learn: 0.0001354 total: 1m 3s remaining: 15.8s 900: learn: 0.0001058 total: 1m 11s remaining: 7.85s 999: learn: 0.0000872 total: 1m 19s remaining: 0us
df_Xtest.head(1)
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 248750 | 154078.0 | 0.046622 | 1.529678 | -0.453615 | 1.282569 | 1.110333 | -0.882716 | 1.04642 | -0.117121 | -0.679897 | -0.923709 | 0.371519 | -0.000047 | 0.512255 | -2.091762 | 0.786796 | 0.159652 | 1.706939 | 0.458922 | 0.037665 | 0.240559 | -0.338472 | -0.839547 | 0.066527 | 0.836447 | 0.07679 | -0.775158 | 0.261012 | 0.058359 | 18.7 |
df_Xtest.head(1)['V15 V18 V3 V24 V1 V8 V4 V14 V2 V6 V9 V20'.split()].round(4)
| V15 | V18 | V3 | V24 | V1 | V8 | V4 | V14 | V2 | V6 | V9 | V20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 248750 | 0.7868 | 0.4589 | -0.4536 | 0.8364 | 0.0466 | -0.1171 | 1.2826 | -2.0918 | 1.5297 | -0.8827 | -0.6799 | 0.2406 |
# Look only first row of test data
# use matplotlib=True to avoid Javascript
idx = 0
shap.force_plot(explainer.expected_value,
shap_values[idx,:],
df_Xtest.iloc[idx,:],
matplotlib=False,
text_rotation=90)
# for this row, the predicted label is -9.33
# red features makes it higher
# blue features makes it smaller.
NUM = 100
shap.force_plot(explainer.expected_value, shap_values[:NUM,:],
df_Xtest.iloc[:NUM,:],matplotlib=False)
shap.summary_plot(shap_values, df_Xtest)
shap.summary_plot(shap_values, df_Xtest, plot_type='bar')
shap.dependence_plot("Amount", shap_values, df_Xtest)
shap.dependence_plot(ind='Time', interaction_index='Amount',
shap_values=shap_values,
features=df_Xtest,
display_features=df_Xtest)
notebook_end_time = time.time()
time_taken = time.time() - notebook_start_time
h,m = divmod(time_taken,60*60)
print('Time taken to run whole noteook: {:.0f} hr {:.0f} min {:.0f} secs'.format(h, *divmod(m,60)))
Time taken to run whole noteook: 0 hr 22 min 40 secs